The appropriate selection of construction methods is a critical factor in the successful completion of any construction project. Artificial intelligence techniques are widely used to assist in the selection of a construction method. This paper proposes the use of the adaptive boosting (AdaBoost) model to select an appropriate retaining wall method suitable for particular construction site conditions, in order to examine the applicability of AdaBoost in construction method selection. To verify its applicability, the proposed model was compared with a support vector machine (SVM) model, which have been attracting attention for their high performance in various classification problems. The AdaBoost model showed a slightly more accurate result than the SVM model in the selection of retaining wall methods, demonstrating that AdaBoost has advantages (e.g., robustness against defective data with missing values) in application to decision support systems. Moreover, the AdaBoost model can be used in future project...

Understanding the structure and behavior of emissions in building systems is the first step toward improving the reliability of the environmental impact assessment of buildings. The shortcomings of current building lifecycle assessment (LCA) research is the lack of understanding of embodied emissions and static analysis. This study presents a methodology for the dynamic LCA of buildings, combined with the system dynamics technique. Dynamic factors related to recurrent embodied emissions are explored through a literature review. Applying the dynamic factors based on the review, a causal map and stock-flow diagram are invented. Collecting the field data and establishing the assumptions based on the literature, a case study is performed for the proposed model. As a result, through dynamic analysis, it was found that recurrent embodied emissions have a considerably different behavior from static ones during their whole life. Additionally, it was found that the environmental impacts changed by more than 10%, according to the variation of the users’ required performance level in sensitivity analysis. This result thoroughly addressed the necessity and appropriateness of dynamic LCA. The dynamic LCA model developed in this study can contribute to the long-term behavioral understanding of the embodied environmental impacts of building LCA.

With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

Over the past decades, automation and robotics have been widely adopted in the construction industry as a promising solution for troubleshooting issues. This field is highly interdisciplinary and rapidly changing, and new concepts and ideas are being actively introduced in association with the existing ones. To reveal new insights, the evolution of knowledge in this field needs to be characterized by identifying important knowledge elements and research trends from published data. This study investigated research trends and network characteristics in the field of construction automation and robotics by using a keyword network analysis based on published articles. We analyzed the structure of the keyword network and identified major research topics and their relationships. Notable findings of the research characteristics are as follows. 1) Popular research themes with a set of closely connected keywords continue to be selected and contribute to the evolution of the network. 2) As an interdisciplinary research area, popular keywords that actively interact with other keywords also bridge different clusters of related research concepts. This study could provide useful insights and a better understanding of the knowledge structure and research trends as well as information on future directions in the construction automation and robotics field.

In recent years, excavation work of high-rise buildings has been becoming increasingly complex in the congested urban areas and frequently exposed to substantial hazards, impediment to safety as well as financial loses. For these reasons, advanced sensor-based systems have been used to accurately monitor and diagnose the failure risk of an excavation work; however, it is often tricky to anticipate and assess the impacts of changeable variables, such as human factors, site conditions, material loadings, and mobile equipment, because of the lack of proper tools to explain the unforeseen phenomena. This study proposed a decision support model to concretize effectively experts’ and practitioners’ subjectivities and to quantify the failure risk. The model is fundamentally constructed on fuzzy analytic hierarchy process, which weights the environmental influences that can derive a failure. The outcomes are used as an input for fuzzy comprehensive operations to compute the quantitative failure risk. Three illustrative cases have been examined to explain the capabilities of the proposed model. The results have shown the possibility that the model can be helpful for developing safety precautions with a warning signal during the planning and controlling stages of a new or ongoing excavation work.